One Stop Anomaly Shop: Anomaly detection using two-phase approach: (a) pre-labeling using statistics, Natural Language Processing and static rules; (b) anomaly scoring using supervised and unsupervised machine learning.

Process behaviour anomaly detection using eBPF and unsupervised-learning Autoencoders

Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.

Detection of network traffic anomalies using unsupervised machine learning

Modern and future vehicles are complex cyber-physical sys-tems. The connection to their outside environment raises many securityproblems that impact our safety directly. In this work, we propose a DeepCAN intrusion detection system framework. We propose a multivariatetime series representation for asynchronous CAN data. This represen-tation enhances the temporal modelling of deep learning architecturesfor anomaly detection. We study different deep learning tasks (super-vised/unsupervised) and compare different architectures, to propose anin-vehicle intrusion detection system that fits constraints of memory andcomputational power of the in-vehicle system. The proposed intrusiondetection system is time window wise: any given time frame is labelledeither anomalous or normal. We conduct experiments with many types ofattacks on an in-vehicle CAN using SynCAn dataset. We show that oursystem yields good results and allow to detect different kinds of attacks.

BNP Paribas Kaggle Data Set Data source: https://www.kaggle.com/c/bnp-paribas-cardif-claims-management Outlier Detection- Ensemble unsupervised learning method - Isolation Forest The isolation algorithm is an unsupervised machine learning method used to detect abnormal anomalies in data such as outliers. This is once again a randomized & recursive partition of the training data in a tree structure. The number of sub samples and tree size is specified and tuned appropriately. The distance to the outlier is averaged calculating an anomaly detection score: 1 = outlier 0 = close to zero are normal data.

Use machine learning to classify malware. Malware analysis 101. Set up a cybersecurity lab environment. Learn how to tackle data class imbalance. Unsupervised anomaly detection. End-to-end deep neural networks for malware classification. Create a machine learning Intrusion Detection System (IDS). Employ machine learning for offensive security. Learn how to address False Positive constraints. Break a CAPTCHA system using machine learning.

Applied unsupervised machine learning algorithms (K-Means Clustering and Isolation Forest) on time series data collected from an Air Handling Unit of a building to detect anomalous behavior of the system. Applied exploratory data analysis using Python to identify non-optimal working conditions of the AHU. Designed an automated anomaly detection system and a corrective strategy to control the AHU effectively.

This code is for paper "HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection" and is currently under review. Please don't quote it or use it for any other purpose.

Deep Learning based technique for Unsupervised Anomaly Detection using DeepAnT and LSTM Autoencoder

There are many studies done to detect anomalies based on logs. Current approaches are mainly divided into three categories: supervised learning methods, unsupervised learning methods, and deep learning methods. Many supervised learning methods are used for log-based anomaly detection.

An implementation of the DeepAnT model, a deep learning approach for unsupervised anomaly detection in time series data, using Python.

Developed a Real-time Intrusion Detection System for Windows that leverages Machine Learning techniques to identify and prevent network intrusions. The system uses a Supervised learning model, Random Forest, to detect known attacks from CICIDS 2018 & SCVIC-APT databases, and an Unsupervised learning model, Autoencoder, for anomaly detection.

Context In the context of textile fabric, rare anomaly can occurs, hence compromising the quality of the tissues. In order to avoid that in some scenario, it is crucial to detect the defect. This dataset is for educational purposes Content Image size: 32x32 or 64x64 classes: ['good', 'color', 'cut', 'hole', 'thread', 'metal contamination'] rotations: 8 different rotations in [0, 20, 40, 60, 80, 100, 120, 140] Given an image size, a train and test dataset are available with randomly generated patches. Source images from the train and test are non-overlapping different tasks are possible: classification of the classes type classification of angles using only "good" images and testing of other classes texture representation learning / self-supervised learning Acknowledgements Based on the public dataset by the MVTec company Paul Bergmann, Michael Fuser, David Sattlegger, Carsten Steger. MVTec AD - A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection; in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019 Inspiration the main goal of this dataset is to explore self-supervised learning on texture images in order to solve anomaly detection problems and learn a robust representation of texture in lieu of traditional image processing features (e.g. glcm, gabor,….)

This repository contains the implementation of the paper "Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning" published in the IEEE Communication Letters.

A Welding Anomaly Detection system using unsupervised Machine Learning and statistical analysis to identify anomalies in welding process curves.

Outiers are rare but are very crucial. In this project, several methods to detect anomalies using Unsupervised Learning where no labelled dataset is given is presented. This work was done between August 2019- November 2019. This later on served as the base project for the Master Thesis which is available in other repository. Unfortunately, I am not open to share code for this one but for master thesis code is public. Hope it helps. As we are moving towards the Industry 4.0 era where Artificial Intelligence(AI) and the Internet of Things(IoT) are crucial and integral parts of the revolution. In this transition phase from manual to the automation of work using different machines, sensors are a very important component and they play a vital role in the setup. The connectivity and flow of data/ information between sensors and devices leads us to witness rapid growth of time-based data are known as time series. In this project we will be implementing the techniques and applications of machine learning and statistical analysis, getting familiar with pandas, matplotlib, NumPy and various other libraries using Python on available sensor data from industries and extract useful information and make it possible to detect outliers and perform conditional monitoring which in-turn will help in reducing cost, optimizing manual labour capacity, increase productivity, availability, reliability and keep downtime minimum. The main aim of the Research Project is to develop online multivariate analysis tool which fetches the data, impute the missing data, eliminates outliers and non- compliant data, perform unsupervised learning and inform the user in case of abnormality i.e., out of control situations.

Anomalies detection (fraud customers) and breast cancer detection using the self-organizing maps, an unsupervised deep learning technique

An Anomaly Based Network Intrusion Detection System (A-NIDS) that uses Unsupervised Learning.

:squirrel: (💳) Strike is a Credit Card Fraud Detection Project that we will be developing for demonstration purposes showing unsupervised machine learning using anomaly detection from sklearn package.

Explore Network Anomaly Detection Project 📊💻. It achieves an exceptional 99.7% accuracy through a blend of supervised and unsupervised learning, extensive feature selection, and model experimentation. Stunning data visualizations using synthetic network traffic data offer insightful representations of anomalies, enhancing network security.

The project is aimed at developing new tools for classifying videos of human-machine interactions in the Internet-of-Things (IOT) domain. Namely, given videos of humans interacting with IoT devices (e.e., smart appliances such as fridge, toaster, washing machines, Alexa, etc), the aim is to (1) design predictive video features, which (2) can be extracted efficiently in real-time to classify videos in terms of the activity being performed (opening or closing a fridge, loading or unloading a washing machine, etc.). The grand goal and motivation for the work is to generate labels for IoT network traffic, simply by training cameras onto IoT devices in the various IoT labs across US universities. Thus, the project aims to solve a main bottleneck in research at the intersection of Machine Learning and IoT, namely, the scarcity of labeled IoT traffic data to solve ML problems such as activity and anomaly detection using supervised or unsupervised detection procedures.

Bank Card Fraud Detection project that uses 'unsupervised anomaly detection' and 'unsupervised & supervised deep learning' techniques to detect anomalous data points.

Anomaly Detection using Unsupervised Machine Learning

engine for anomaly detection in time series using unsupervised learning

Use some anomaly detection algorithms/unsupervised machine learning techniques to detect the market events, based on the price, volume, and volatility.

This project implements a real-time anomaly detection system using unsupervised machine learning models and AI-driven solutions. It integrates components such as data ingestion from Kafka, model training, anomaly detection, real-time alerting, object detection in CCTV footage using YOLO, and deployment to AWS Lambda or Google Cloud.

Clustering is a popular unsupervised machine learning technique used to group similar data points based on specific criteria. It has many applications in various fields such as customer segmentation, image recognition, and anomaly detection. K-means clustering is a widely used clustering algorithm that partitions the data into k clusters, where eac

Unsupervised Anomaly Detection in Multivariate Time Series Data using Deep Learning

A notebook using many unsupervised learning techniques. PCA, K-means, Gaussian Mixtures. Clustering, dimensionality reduction, anomaly detection